---
title: Parallel Workflow
description: Independent, concurrent tasks that can execute simultaneously for improved efficiency
---

**Example Use-Cases**: Multi-source research, parallel analysis, concurrent data processing

Parallel workflows maintain deterministic results while dramatically reducing execution time for independent operations.

<img 
  className="block dark:hidden" 
  src="/images/workflows-parallel-steps-light.png" 
  alt="Workflows parallel steps diagram"
/>
<img 
  className="hidden dark:block" 
  src="/images/workflows-parallel-steps.png" 
  alt="Workflows parallel steps diagram"
/>

## Example

```python parallel_workflow.py
from agno.workflow import Parallel, Step, Workflow

workflow = Workflow(
    name="Parallel Research Pipeline",
    steps=[
        Parallel(
            Step(name="HackerNews Research", agent=hn_researcher),
            Step(name="Web Research", agent=web_researcher),
            Step(name="Academic Research", agent=academic_researcher),
            name="Research Step"
        ),
        Step(name="Synthesis", agent=synthesizer),  # Combines the results and produces a report
    ]
)

workflow.print_response("Write about the latest AI developments", markdown=True)
```

## Developer Resources

- [Parallel Steps Workflow](/examples/concepts/workflows/04-workflows-parallel-execution/parallel_steps_workflow)

## Reference

For complete API documentation, see [Parallel Steps Reference](/reference/workflows/parallel-steps).